Estimating Genomic Category Probabilities from Fluorescent in Situ Hybridization Counts with Misclassification
针对荧光原位杂交（FISH）数据中常见的分类错误，提出一种利用已知类型细胞（如正常受试者）的背景数据来校正误分类、估计各基因组类别细胞比例并进行假设检验的统计方法，并应用于慢性粒细胞白血病患者的血细胞计数数据。
SUMMARY Fluorescent in situ hybridization (FISH) is used in many medical settings to identify the genetic or chromosomal abnormality characterizing a disease. FISH techniques may be used to classify a sample of a patient's cells into genomic categories, one or more of which is associated with the disease. The clinical goal is to determine whether there is a positive proportion of diseased cells in the patient, or to estimate this proportion. Unfortunately, such data are often subject to classification error inherent in FISH methodology. However, when additional data are available from cells of known type, typically from normal subjects, this information may be combined with the patient's data to perform the desired inference while correcting for misclassification. We provide a method for estimating the proportions of cells of each category and testing whether a particular proportion is positive in each of several patients when such background data are available. Our approach is to model the misclassification probabilities, jointly to estimate the model parameters and each patient's cell type proportions by using maximum likelihood and to use this to obtain likelihood ratio tests and confidence intervals. The method is applied to blood cell count data from chronic myelogenous leukaemia patients, where FISH is used to identify the chromosomal translocation characterizing the disease.